Overview

Dataset statistics

Number of variables11
Number of observations157
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 KiB
Average record size in memory63.8 B

Variable types

Numeric11

Alerts

Year has constant value "2010" Constant
df_index is highly correlated with MonthHigh correlation
Ozone is highly correlated with TemperatureHigh correlation
Month is highly correlated with df_indexHigh correlation
Temperature is highly correlated with OzoneHigh correlation
Weather_C is highly correlated with Weather_SHigh correlation
Weather_PS is highly correlated with Weather_SHigh correlation
Weather_S is highly correlated with Weather_C and 1 other fieldsHigh correlation
df_index is highly correlated with MonthHigh correlation
Ozone is highly correlated with Wind and 1 other fieldsHigh correlation
Wind is highly correlated with OzoneHigh correlation
Month is highly correlated with df_indexHigh correlation
Temperature is highly correlated with OzoneHigh correlation
Weather_C is highly correlated with Weather_SHigh correlation
Weather_PS is highly correlated with Weather_SHigh correlation
Weather_S is highly correlated with Weather_C and 1 other fieldsHigh correlation
df_index is highly correlated with MonthHigh correlation
Ozone is highly correlated with TemperatureHigh correlation
Month is highly correlated with df_indexHigh correlation
Temperature is highly correlated with OzoneHigh correlation
Weather_C is highly correlated with Weather_SHigh correlation
Weather_PS is highly correlated with Weather_SHigh correlation
Weather_S is highly correlated with Weather_C and 1 other fieldsHigh correlation
df_index is highly correlated with Ozone and 3 other fieldsHigh correlation
Ozone is highly correlated with df_index and 3 other fieldsHigh correlation
Wind is highly correlated with Ozone and 1 other fieldsHigh correlation
Month is highly correlated with df_index and 2 other fieldsHigh correlation
Day is highly correlated with df_index and 1 other fieldsHigh correlation
Temperature is highly correlated with df_index and 4 other fieldsHigh correlation
Weather_C is highly correlated with Weather_PS and 1 other fieldsHigh correlation
Weather_PS is highly correlated with Weather_C and 1 other fieldsHigh correlation
Weather_S is highly correlated with Weather_C and 1 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
Weather_C has 108 (68.8%) zeros Zeros
Weather_PS has 110 (70.1%) zeros Zeros
Weather_S has 96 (61.1%) zeros Zeros

Reproduction

Analysis started2022-12-06 11:46:29.556350
Analysis finished2022-12-06 11:46:43.827488
Duration14.27 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct157
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.00636943
Minimum1
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-12-06T17:16:43.940423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.8
Q140
median79
Q3118
95-th percentile149.2
Maximum158
Range157
Interquartile range (IQR)78

Descriptive statistics

Standard deviation45.47717049
Coefficient of variation (CV)0.5756139767
Kurtosis-1.198795274
Mean79.00636943
Median Absolute Deviation (MAD)39
Skewness0.0008506369735
Sum12404
Variance2068.173036
MonotonicityStrictly increasing
2022-12-06T17:16:44.134085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.6%
1091
 
0.6%
1021
 
0.6%
1031
 
0.6%
1041
 
0.6%
1051
 
0.6%
1061
 
0.6%
1071
 
0.6%
1081
 
0.6%
1101
 
0.6%
Other values (147)147
93.6%
ValueCountFrequency (%)
11
0.6%
21
0.6%
31
0.6%
41
0.6%
51
0.6%
61
0.6%
71
0.6%
81
0.6%
91
0.6%
101
0.6%
ValueCountFrequency (%)
1581
0.6%
1561
0.6%
1551
0.6%
1541
0.6%
1531
0.6%
1521
0.6%
1511
0.6%
1501
0.6%
1491
0.6%
1481
0.6%

Ozone
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct67
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.19745223
Minimum1
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-12-06T17:16:44.325075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q121
median31
Q345
95-th percentile97
Maximum168
Range167
Interquartile range (IQR)24

Descriptive statistics

Standard deviation28.78199212
Coefficient of variation (CV)0.7342822169
Kurtosis3.064808929
Mean39.19745223
Median Absolute Deviation (MAD)11
Skewness1.663989806
Sum6154
Variance828.4030704
MonotonicityNot monotonic
2022-12-06T17:16:44.415958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3139
24.8%
236
 
3.8%
185
 
3.2%
144
 
2.5%
164
 
2.5%
134
 
2.5%
214
 
2.5%
204
 
2.5%
443
 
1.9%
73
 
1.9%
Other values (57)81
51.6%
ValueCountFrequency (%)
11
 
0.6%
41
 
0.6%
61
 
0.6%
73
1.9%
81
 
0.6%
93
1.9%
101
 
0.6%
113
1.9%
122
1.3%
134
2.5%
ValueCountFrequency (%)
1681
0.6%
1351
0.6%
1221
0.6%
1181
0.6%
1151
0.6%
1101
0.6%
1081
0.6%
972
1.3%
961
0.6%
911
0.6%

Solar
Real number (ℝ≥0)

Distinct118
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean185.9745223
Minimum7
Maximum334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-12-06T17:16:44.521503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile24.8
Q1127
median199
Q3255
95-th percentile308.2
Maximum334
Range327
Interquartile range (IQR)128

Descriptive statistics

Standard deviation87.04478283
Coefficient of variation (CV)0.468046815
Kurtosis-0.8318933218
Mean185.9745223
Median Absolute Deviation (MAD)61
Skewness-0.4438554159
Sum29198
Variance7576.794219
MonotonicityNot monotonic
2022-12-06T17:16:44.624447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1997
 
4.5%
2384
 
2.5%
2594
 
2.5%
1903
 
1.9%
2233
 
1.9%
2203
 
1.9%
1753
 
1.9%
1312
 
1.3%
922
 
1.3%
1912
 
1.3%
Other values (108)124
79.0%
ValueCountFrequency (%)
71
0.6%
81
0.6%
131
0.6%
141
0.6%
191
0.6%
201
0.6%
242
1.3%
251
0.6%
271
0.6%
311
0.6%
ValueCountFrequency (%)
3341
0.6%
3321
0.6%
3231
0.6%
3222
1.3%
3201
0.6%
3141
0.6%
3131
0.6%
3071
0.6%
2991
0.6%
2951
0.6%

Wind
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.929936306
Minimum1.7
Maximum20.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-12-06T17:16:44.963410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile4.6
Q17.4
median9.7
Q311.5
95-th percentile15.5
Maximum20.7
Range19
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation3.505187821
Coefficient of variation (CV)0.3529919743
Kurtosis0.114443177
Mean9.929936306
Median Absolute Deviation (MAD)2.3
Skewness0.3650639762
Sum1559
Variance12.28634166
MonotonicityNot monotonic
2022-12-06T17:16:45.077069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11.515
 
9.6%
812
 
7.6%
7.411
 
7.0%
10.311
 
7.0%
9.711
 
7.0%
6.910
 
6.4%
6.38
 
5.1%
9.28
 
5.1%
10.98
 
5.1%
8.68
 
5.1%
Other values (21)55
35.0%
ValueCountFrequency (%)
1.71
 
0.6%
2.31
 
0.6%
2.81
 
0.6%
3.41
 
0.6%
41
 
0.6%
4.11
 
0.6%
4.64
2.5%
5.13
 
1.9%
5.73
 
1.9%
6.38
5.1%
ValueCountFrequency (%)
20.71
 
0.6%
20.11
 
0.6%
18.41
 
0.6%
16.63
 
1.9%
16.11
 
0.6%
15.53
 
1.9%
14.98
5.1%
14.36
3.8%
13.85
3.2%
13.23
 
1.9%

Month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.01910828
Minimum5
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size756.0 B
2022-12-06T17:16:45.203537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median7
Q38
95-th percentile9
Maximum9
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.434337771
Coefficient of variation (CV)0.2043475771
Kurtosis-1.325691883
Mean7.01910828
Median Absolute Deviation (MAD)1
Skewness-0.02068114319
Sum1102
Variance2.057324841
MonotonicityNot monotonic
2022-12-06T17:16:45.289469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
933
21.0%
532
20.4%
731
19.7%
831
19.7%
630
19.1%
ValueCountFrequency (%)
532
20.4%
630
19.1%
731
19.7%
831
19.7%
933
21.0%
ValueCountFrequency (%)
933
21.0%
831
19.7%
731
19.7%
630
19.1%
532
20.4%

Day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.92993631
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-12-06T17:16:45.380553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile29.2
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.974404237
Coefficient of variation (CV)0.5633672392
Kurtosis-1.225943132
Mean15.92993631
Median Absolute Deviation (MAD)8
Skewness-0.02655457059
Sum2501
Variance80.53993141
MonotonicityNot monotonic
2022-12-06T17:16:45.461628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
16
 
3.8%
296
 
3.8%
276
 
3.8%
266
 
3.8%
175
 
3.2%
305
 
3.2%
285
 
3.2%
255
 
3.2%
245
 
3.2%
235
 
3.2%
Other values (21)103
65.6%
ValueCountFrequency (%)
16
3.8%
25
3.2%
35
3.2%
45
3.2%
55
3.2%
65
3.2%
75
3.2%
85
3.2%
95
3.2%
105
3.2%
ValueCountFrequency (%)
313
1.9%
305
3.2%
296
3.8%
285
3.2%
276
3.8%
266
3.8%
255
3.2%
245
3.2%
235
3.2%
225
3.2%

Year
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010
Minimum2010
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-12-06T17:16:45.534172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12010
median2010
Q32010
95-th percentile2010
Maximum2010
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean2010
Median Absolute Deviation (MAD)0
Skewness0
Sum315570
Variance0
MonotonicityIncreasing
2022-12-06T17:16:45.597059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
2010157
100.0%
ValueCountFrequency (%)
2010157
100.0%
ValueCountFrequency (%)
2010157
100.0%

Temperature
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.74522293
Minimum56
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-12-06T17:16:45.682490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile60.6
Q172
median79
Q384
95-th percentile92
Maximum97
Range41
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.405334315
Coefficient of variation (CV)0.1209763631
Kurtosis-0.4128496196
Mean77.74522293
Median Absolute Deviation (MAD)6
Skewness-0.3447816805
Sum12206
Variance88.46031357
MonotonicityNot monotonic
2022-12-06T17:16:45.779934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
8111
 
7.0%
7610
 
6.4%
829
 
5.7%
778
 
5.1%
867
 
4.5%
786
 
3.8%
796
 
3.8%
675
 
3.2%
735
 
3.2%
805
 
3.2%
Other values (30)85
54.1%
ValueCountFrequency (%)
561
 
0.6%
573
1.9%
582
1.3%
592
1.3%
613
1.9%
622
1.3%
631
 
0.6%
642
1.3%
652
1.3%
663
1.9%
ValueCountFrequency (%)
971
 
0.6%
961
 
0.6%
942
 
1.3%
933
1.9%
925
3.2%
912
 
1.3%
903
1.9%
892
 
1.3%
883
1.9%
875
3.2%

Weather_C
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3121019108
Minimum0
Maximum1
Zeros108
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size285.0 B
2022-12-06T17:16:45.857121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.464833899
Coefficient of variation (CV)1.489365758
Kurtosis-1.346749352
Mean0.3121019108
Median Absolute Deviation (MAD)0
Skewness0.8188842555
Sum49
Variance0.2160705537
MonotonicityNot monotonic
2022-12-06T17:16:45.923451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0108
68.8%
149
31.2%
ValueCountFrequency (%)
0108
68.8%
149
31.2%
ValueCountFrequency (%)
149
31.2%
0108
68.8%

Weather_PS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2993630573
Minimum0
Maximum1
Zeros110
Zeros (%)70.1%
Negative0
Negative (%)0.0%
Memory size285.0 B
2022-12-06T17:16:45.990276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4594445944
Coefficient of variation (CV)1.534740454
Kurtosis-1.233254014
Mean0.2993630573
Median Absolute Deviation (MAD)0
Skewness0.8846586028
Sum47
Variance0.2110893353
MonotonicityNot monotonic
2022-12-06T17:16:46.049651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0110
70.1%
147
29.9%
ValueCountFrequency (%)
0110
70.1%
147
29.9%
ValueCountFrequency (%)
147
29.9%
0110
70.1%

Weather_S
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3885350318
Minimum0
Maximum1
Zeros96
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size285.0 B
2022-12-06T17:16:46.096535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.488976974
Coefficient of variation (CV)1.258514507
Kurtosis-1.809968786
Mean0.3885350318
Median Absolute Deviation (MAD)0
Skewness0.4617936296
Sum61
Variance0.2390984811
MonotonicityNot monotonic
2022-12-06T17:16:46.169103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
096
61.1%
161
38.9%
ValueCountFrequency (%)
096
61.1%
161
38.9%
ValueCountFrequency (%)
161
38.9%
096
61.1%

Interactions

2022-12-06T17:16:42.656582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:32.970513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.923279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.932423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.027096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.938833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.920824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.167322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.085300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.869621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.868735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.740626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.061563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.009213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:35.018753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.105743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.034847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:38.005003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.265773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.153493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.955742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.943900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.814667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.142213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.088037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:35.101023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.188429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.118782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:38.089352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.362874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.227475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.036098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.011557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.888624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.230651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.163065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:35.180810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.281944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.207778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:38.394037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.450324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.295062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.119550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.093308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.966130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.309129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.241978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:35.326085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.356819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.293338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:38.558593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.540718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.371828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.190116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.167782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:43.041829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.404090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.327067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:35.502117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.435907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.378591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:38.680460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.633198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.444846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.275008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.245496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:43.125335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.500576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.401351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:35.635295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.513771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.453430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:38.766014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.705379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.512501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.344565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.312003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:43.203822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.596881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.477261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:35.721356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.613324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.544708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:38.867732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.791449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.578243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.422768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.389601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:43.266930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.678695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.557709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:35.793713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.689697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.617039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:38.937154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.866800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.647171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.664924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.455465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:43.351434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.743594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.634557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:35.868740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.773638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.711401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.007737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.938611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.721094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.735016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.525036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:43.434641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:33.838001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:34.855979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:35.946757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:36.855701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:37.824017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:39.069749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.010908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:40.794230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:41.788967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-06T17:16:42.594793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-12-06T17:16:46.240750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-06T17:16:46.465625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-06T17:16:46.687162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-06T17:16:46.918288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-06T17:16:43.563422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-06T17:16:43.735287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexOzoneSolarWindMonthDayYearTemperatureWeather_CWeather_PSWeather_S
0141.0190.07.451201067001
1236.0118.08.052201072100
2312.0149.012.653201074010
3418.0313.011.554201062001
4531.0199.014.355201056001
5628.0199.014.956201066100
6723.0299.08.657201065010
7819.099.013.858201059100
898.019.020.159201061010
91031.0194.08.6510201069001

Last rows

df_indexOzoneSolarWindMonthDayYearTemperatureWeather_CWeather_PSWeather_S
14714814.020.016.6925201063010
14814930.0193.06.9926201070100
14915031.0145.013.2927201077010
15015114.0191.014.3928201075001
15115218.0131.08.0929201076010
15215320.0223.011.5930201068001
15315441.0190.07.451201067100
15415530.0193.06.9926201070010
15515631.0145.013.2927201077001
15615818.0131.08.0929201076100